Section G. Comparing Means between More than Two Independent Populations

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1 This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this site. Copyright 2009, The Johns Hopkins University and John McGready. All rights reserved. Use of these materials permitted only in accordance with license rights granted. Materials provided AS IS ; no representations or warranties provided. User assumes all responsibility for use, and all liability related thereto, and must independently review all materials for accuracy and efficacy. May contain materials owned by others. User is responsible for obtaining permissions for use from third parties as needed.

2 Section G Comparing Means between More than Two Independent Populations

3 Motivating Example Suppose you are interested in the relationship between smoking and mid-expiratory flow (FEF), a measure of pulmonary health Suppose you recruit study subjects and classify them into one of six smoking categories Nonsmokers (NS) Passive smokers (PS) Non-inhaling smokers (NI) Light smokers (LS) Moderate smokers (MS) Heavy smokers (HS) 3

4 Motivating Example You are interested in whether differences exist in mean FEF amongst the six groups Main outcome variable is mid-expiratory flow (FEF) in liters per second 4

5 Motivating Example One strategy is to perform lots of two-sample t-tests (for each possible two-group comparison) In this example, there would be 15 comparisons you would need to do! NS to PS, NS to NI, and so on... 5

6 Motivating Example It would be nice to have one catch-all test Something which would tell you whether there were any differences amongst the six groups If so, you could then do group to group comparisons to look for specific group differences 6

7 Extension of the Two-Sample t-test Analysis of variance (One-Way ANOVA) The t-test compares means in two populations ANOVA compares means amongst more than two populations with one test The p-value from ANOVA helps answer the question Are there any differences in the means among the populations? 7

8 Extension of the Two-Sample t-test General idea behind ANOVA, comparing means for k-groups (k > 2): H o : µ 1 = µ 2 =... µ k H A : At least one mean different 8

9 Example Smoking and FEF (Forced Mid-Expiratory Flow Rate)* A sample of over 3,000 persons was classified into one of six smoking categorizations based on responses to smoking related questions Source: * White, J.R., Froeb, H.F. (1980). Small-airways dysfunction in non-smokers chronically exposed to tobacco smoke, New England Journal of Medicine 302: 13. 9

10 Example 1 Nonsmokers (NS) Passive smokers (PS) Non-inhaling smokers (NI) Light smokers (LS) Moderate smokers (MS) Heavy smokers (HS) 10

11 Example 1 Smoking and FEF From each smoking group, a random sample of 200 men was drawn (except for the non-inhalers, as there were only 50 male non-inhalers in the entire sample of 3,000) FEF measurements were taken on each of the subjects 11

12 Example 1 Table Data summary Group Mean FEF SD FEF (L/s) (L/s) n NS PS NI LS MS HS Based on a one-way analysis of variance, there are statistically significant differences in FEF levels among the six smoking groups (p <.001) 12

13 What s the Rationale behind Analysis of Variance? The variation in the sample means between groups is compared to the variation within a group If the between group variation is a lot bigger than the within group variation, that suggests there are some differences among the populations 13

14 Analysis of Variance 14

15 Summary: Smoking and FEF Statistical methods 200 men were randomly selected from each of five smoking classification groups (non-smoker, passive smokers, light smokers, moderate smokers, and heavy smokers), as well as 50 men classified as non-inhaling smokers for a study designed to analyze the relationship between smoking and respiratory function 15

16 Summary: Smoking and FEF Statistical Methods Analysis of variance was used to test for any differences in FEF levels amongst the six groups of men Individual group comparisons were performed with a series of two sample t-tests, and 95% confidence intervals were constructed for the mean difference in FEF between each combination of groups Analysis of variance showed statistically significant (p <.001) differences in FEF between the six groups of smokers Non-smokers had the highest mean FEF value, 3.78 L/s, and this was statistically significantly larger than the five other smokingclassification groups 16

17 Summary: Smoking and FEF Results Analysis of variance showed statistically significant (p <.001) differences in FEF between the six groups of smokers Non-smokers had the highest mean FEF value, 3.78 L/s, and this was statistically significantly larger than the five other smokingclassification groups The mean FEF value for non-smokers was 1.19 L/s higher than the mean FEF for heavy smokers (95% CI L/s), the largest mean difference between any two smoking groups Confidence intervals for all smoking group FEF comparisons are in Table 1 17

18 Example 2 FEV1 and three medical centers* Data was collected on 63 patients with coronary artery disease at 3 difference medical centers (Johns Hopkins, Ranchos Los Amigos Medical Center, St. Louis University School of Medicine) Purpose of study to investigate effects of carbon monoxide exposure on these patients Prior to analyzing CO effects data, researchers wished to compare the respiratory health of these patients across the three medical centers Source: * Pagano, M., Gauvreau, K. (2000). Principles of biostatistics. Duxbury Press. 18

19 Example 2 Snippet of data in Stata 19

20 Boxplots FEV1 values by center 20

21 Example 2 ANOVA with Stata syntax oneway outcome_var group_var 21

22 Example 2 ANOVA with Stata syntax oneway outcome_var group_var 22

23 Example 2 FEV and 3 medical centers 95% CIs for FEV1 by medical center 23

Comparing Means among Two (or More) Independent Populations. John McGready Johns Hopkins University

Comparing Means among Two (or More) Independent Populations. John McGready Johns Hopkins University This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this